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Qualitative Marketing Research - PRAD 405, Lecture 7

• Qualitative Research Sampling: Qualitative samples aren't randomly selected; they are chosen based on specific characteristics to deeply examine phenomena. Different types of qualitative samples exist.

Purposive Sampling

• Selection: Individuals or cases are deliberately chosen to best represent the phenomenon or topic under study.
• Expertise: Focus is on individuals with experience or knowledge that adds significant value to the research.
• Relevance: Selected participants directly relate to the marketing topic under investigation (e.g., customers using a specific product).

Characteristics of Purposive Sampling in Marketing Research

• Purposeful Selection: Participants are chosen based on the researcher’s market knowledge and understanding of the topic, prioritizing individuals offering insightful information.
• No Generalization: The goal is not to represent the entire population, but to gain an in-depth understanding of specific cases or viewpoints.

When to Use Purposive Sampling in Marketing

• Exploratory Studies: Investigating new phenomena like purchasing trends.
• Key Customer Analysis: Understanding why certain customer segments display loyalty or have particular impressions of a product or service.
• Product Concept Testing: Gathering opinions from a small group of interested customers about a new product.
• Marketing Campaign Evaluation: Targeting customers impacted by advertising to gauge campaign effectiveness.

Examples of Purposive Sampling in Marketing Research

• Brand Loyalty: Selecting highly loyal customers to investigate their motivations.
• Social Media Influencers: Targeting influential social media figures to evaluate a new product or service.
• Dissatisfied Customers: Conducting research with a small group of dissatisfied customers to understand reasons for dissatisfaction.

Snowball Sampling

• Recruitment: The researcher gathers initial data from a small group of individuals (initial sample) who then refer other participants to the research. This progressively increases the sample size.
• Use Cases: In marketing, useful for cases where there’s difficulty reaching target audiences directly (specialized market sectors, rare demographic groups, or inaccessible communities). It is also employed in studies pertaining to consumer behavior or understanding target demographics with traditional sampling methods.

Convenience Sampling

• Selection: Individuals or cases readily available or easy to reach are chosen for the study.
• Accessibility: Individuals located within a specific geographical or social proximity are selected for the research. It is a non-probability or non-random sampling method.
• Use in Marketing: Common in exploratory research, especially when time or budget is limited.

Advantages of Convenience Sampling in Marketing

• Fast and Low Cost: Rapid data collection with reduced cost as it involves readily available participants.
• Suitable for Exploratory Research: Helpful in getting quick feedback for pilot studies.

Disadvantages of Convenience Sampling in Marketing

• Bias: The sample may not accurately represent the larger target market due to selection criteria. It can be skewed due to self-selection biases.
• Limited Generalization: Results are not easily generalized or applied to the wider population.
• Distorted Response: The sample might not be diverse enough or may show repeated answer patterns, which means the data is not reliable or accurate.

Quota Sampling

• Method: Dividing the target population into segments, and ensuring a certain number (quota) of participants from each segment.
• Criteria: Characteristics like age, gender, income, or specific behavioral attributes are used to define groups, and a particular number of subjects are drawn from each group until preset quotas are filled. This is a non-probability sampling method.
• Use in Marketing: Employing this method ensures appropriate representation of various groups within a target market or population.

Advantages of Quota Sampling in Marketing

• Good Representation: It ensures the inclusion of representatives across various groups, which leads to more comprehensive insights or results regarding the population.
• Speed and Efficiency: Relatively faster and less complex than random sampling methods considering it uses predefined quota, speeding up the data collection processes.
• Adaptability: The research can be adapted to fit specific needs by allocating quotas based on the importance of demographics or behaviors.

Disadvantages of Quota Sampling in Marketing

• Potential Bias: Since selection isn't random, there's a chance of biases reflecting specific segment characteristics and not the wider demographic.
• Limited Generalization: Sample results may not perfectly reflect the entire target population if not done precisely with respect to sample quotas.
• Difficulties in Precise Quotas: Defining and achieving precise quotas to accurately represent each segment can be challenging for complex target markets.

Maximum Variation Sampling

• Goal: Ensuring the sample represents a wide range of potential variations in the target population, encompassing diverse characteristics and opinions.
• Application: In marketing, useful for understanding the diversity of customer preferences, responses, and behaviors within specific market sectors.
• Diverse Participants: Includes individuals or cases that vary significantly in terms of factors like age, gender, geographic location, or purchase histories.

Advantages of Maximum Variation Sampling in Marketing Research

• Comprehensive Insights: The diversity of perspectives helps provide a more complete picture of consumer behavior.
• Enhanced Generalization: The wide variety of participant viewpoints strengthens the ability to draw accurate conclusions and generalize these results.
• Reduced Bias: The diverse sample reduces systematic biases by integrating representatives from various customer demographics and behavior classes.

Disadvantages of Maximum Variation Sampling in Marketing

• Complex Planning: Identifying and selecting participants who represent this wide range of variation could be complex and time-consuming.
• Data Analysis Challenges: Analyzing data from a highly diverse sample can be more complex computationally and require significant effort or resources.